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Can Microservices Revolutionize Data Management in the Enterprise?

The need to develop fast paced quality applications is ubiquitous in the IT sector. From large scale cloud services to contrarian startups, every organization has the term “microservices” on their mind. We slowly want to shift away from the traditional monolithic software arrangement to loosely coupled services.

Gone are the days when the entire developer team worked on a mainframe platform for app development. Applications have now become complex, increasing the need to bring in different data sources and break down heavy computational coding. Add to that the rise of artificial intelligence and machine learning reaching every business function. But, developers have always been using monolithic approaches, and it is challenging to snap into a different mechanism.

Microservice architectures are fast growing into a necessity for data management in the enterprise. By breaking down a vast suite of features into discrete functions, where each runs as its own service that isn’t attached to a particular server or condition, developers are able to create a loosely coupled system of autonomous functions.

The total amount of digital data created worldwide will rise to 163 zettabytes according to IDC. Also predicted is the amount of the global datasphere that’s subject to data analysis will grow by a factor of 50. Leveraging machine learning and microservices will be key to addressing the staggering data and analysis workloads that will be required from these emerging trends.

Here’s why enterprises may want to move to a microservice architecture to ensure they are effectively managing data employing features of AI and machine learning:  

  • Agility and speed: We know that pace of change requirements are accelerating for all enterprises, whether it’s building an application, dashboard, portal or just the response time to call data. Sequestering functionality into services that perform specific tasks against specific data is conducive to rapid development and enhancements. Iterating on models, learning what is working or not, and iterating again can be done quickly.
  • Versatile algorithms: Algorithms can be adjusted independently of other services. This is very helpful for managing models and quickly tweaking based on observed data results.
  • Computational scaling: The scaling and speed benefits are huge while employing AI. Separate containerized services each with a unique scope to ingest, optimize, predict, or whatever outcomes are required, allows for superior scaling and parallel processing capabilities. For example, you might have a separate service for executing machine learning models against particular data set, another for ranking or scoring results, another for detecting anomalies, and so on. Each one of these could be scaled separately. And, as mentioned earlier, making changes and new iteration deployments can be made rapidly without impacting the rest of the system.

The benefits of using microservices are massive for data scientists because it allows them to gain insights into sometimes unfathomable volumes of data at a speed and scale that could never be accomplished with traditional manual or monolithic tools. The potential for microservices architecture ultimately ties to digital transformation – of which data access is the key driver. Microservices architecture is a way to rapidly access and handle data to meet the high pace of change at competitive businesses.

About the Author

Mike Duensing is the Chief Technology Officer and Executive Vice President of Engineering at Skuid, the leading no-code cloud application platform. Mike has been building market-winning SaaS platforms for over 30 years. As a pioneer of internet business software, he’s led teams from small squads to large global organizations in companies including EMC, Documentum, Mindjet, and Standard & Poors. A graduate of UC Berkeley, Mike also enjoys fine coffee.

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